Sometime in late 2024, reports started to emerge that Salt Typhoon, the notorious APT group, breached several big US telcos. One of the entry points for the attackers was an older Cisco vulnerability, CVE-2018-0171.
Although it would be preposterous to deny the threat actor’s high technical capabilities, it wasn’t sophistication that got them inside telcos’ environments. It was an unaddressed 6-year-old vulnerability for which there was an available patch. Had the targets patched this weakness on time, they probably wouldn’t have been victims at all.
Of course, the fact that they hadn’t patched it for so long implies that they might have needed the exact outdated version of Cisco’s software to run their daily operations. But then, this becomes a question of protecting critical outdated software rather than simply protecting software in general.
Wouldn’t it be nice if you could extend the life and usage of vital outdated software and protect everything else — without being completely dependent on patching? Is that even possible?
We’re here to tell you that it is possible, thanks to autonomous exposure mitigation. Read on to discover how this is achieved.
What Is Autonomous Exposure Mitigation?
Autonomous exposure mitigation refers to the proactive and largely self-operating processes and technologies that identify, analyze, and reduce an organization’s attack surface without human intervention.
Instead of having to depend on manual intervention, it monitors your IT environment 24/7 to detect vulnerabilities and different exposures, taking actions to mitigate the identified security risks on its own.
Think of autonomous exposure mitigation as a self-healing immune system for your digital assets. When it detects a vulnerability, like an open port or a software flaw, it doesn’t simply flag it. Instead, it automatically takes steps to close the port, implement virtual patching, or isolate the vulnerable system to prevent possible exploitation.
As the name suggests—nomen est omen—its primary goal is to prioritize and speed up remediation in the form of mitigation. However, it also promotes immediate or near real-time detection and response, allowing human specialists to focus on more strategic goals. For this to become a reality, autonomous exposure mitigation takes advantage of the advanced technologies of artificial intelligence and machine learning. But more on that later.
Keep in mind that autonomous exposure mitigation is not universally recognized as a standalone category within cybersecurity just yet. For now, you can think of it as part of a broader tendency toward automated cybersecurity and self-healing systems. Essentially, it’s exposure management done right.
So, on that note, would you find a separate security product category under this name had you searched for it? Probably not.
Security professionals and vendors are starting to integrate the principles of autonomous exposure mitigation into their programs. However, they don’t have a specialized solution to point out within their security stacks.
Think of this situation this way: You might not have a separate automatic driving category in the automotive industry. But “lane keeping assist (LKA)” and “emergency steering assist” are important autonomous features integrated into vehicles. Similarly, for most, autonomous exposure mitigation is a key set of features and functionalities built into various cybersecurity solutions.
But there’s one exception: Virsec.
Virsec has pioneered OTTOGUARD.AI, a workload patchless mitigation platform that can be considered the first purpose-built autonomous exposure mitigation platform. In the following sections, when describing all the potential of autonomous exposure mitigation, we’ll depict largely but not exclusively this platform’s capabilities, as it is currently the most (and only) complete solution of this kind.
In essence, while “autonomous exposure mitigation software” might not yet be a distinct product category, the concept is vital and increasingly important in modern security, representing a resolute step forward in how cybersecurity approaches risk reduction.
Core Features of Autonomous Exposure Mitigation
Autonomous exposure mitigation fundamentally relies on automation to achieve its goals. That involves:
- Continuous real-time monitoring of your digital landscape to identify vulnerabilities and active threats without the help of human intervention.
- Immediate responses upon detection without waiting for manual involvement.
The responses can range from automatically reconfiguring security settings to isolating compromised systems to blocking malicious code and processes. The speed and consistency of the automated actions do not leave much room for attackers to exploit exposures and cause harm—a critical advantage over manual security workflows.
A key element enabling the effectiveness of autonomous exposure mitigation is the integration of self-learning systems—AI and ML engines based on tested-and-proven algorithms. These systems are capable of analyzing oceans of data, including threat intelligence feeds, security logs, and system behavior. This capability allows them to identify patterns, anomalies, and emerging threats that static rule-based systems might miss.
By continuously learning and adapting protection to new attack vectors and evolving threats, self-learning systems become more accurate and proactive in identifying and responding to sophisticated — as well as unsophisticated — cyber threats over time. Ultimately, the core objective of autonomous exposure mitigation is to deliver proactive protection, that is, to prevent security incidents before they happen and cause practical damage. This proactive stance shifts the security paradigm from a reactive incident response to a state of constant risk reduction.
Why Autonomous Exposure Mitigation Is Necessary
The merits of autonomous exposure mitigation need to be considered vis-à-vis two realities: the limitations of standard exposure management; and the complexity and speed of today’s and, with a little creativity, tomorrow’s cyber threats.
Autonomous exposure mitigation has emerged out of the necessity to address the same problems that exposure management tries to solve, but in a way that is more creative, effective, and appropriate to the scale and speed of the contemporary threat landscape.
The Limitations of Standard Exposure Management
Standard exposure management often operates reactively. Conventional tools conduct periodic scans and assessments to identify vulnerabilities and misconfigurations. That means that organizations address security risks only after they have already manifested within the environment.
The time lag between a vulnerability’s emergence and its detection through scheduled scans can be long, leaving the gates of your digital assets wide open to attackers’ exploits. By the time you confirm there’s an actual security risk, you may be far past preventing an initial compromise and lateral movement.
Another problem is standard exposure management’s heavy reliance on manual processes. If your security analysts have to configure and initiate scans manually, analyze the resulting data, prioritize remediation efforts, and implement fixes on their own, you have a serious time disadvantage against threat actors.
Simply put, human intervention entails grave delays and scalability challenges. The time spent on manual analysis and remediation directly affects the organization’s response time to critical vulnerabilities. As malicious actors rapidly change their modes of action to be more effective, manual processes become bottlenecks, hindering the ability to address emerging risks across a complex and expanding digital landscape efficiently.
On the same note, standard exposure management struggles to keep pace with the evolving nature of modern security threats. Attackers constantly develop new techniques and exploit zero-day vulnerabilities that may not be immediately recognized by scanning tools and signature-based detection methods. More often than not, the speed at which new threats emerge and spread outstrips the cadence of traditional exposure assessments and patching cycles.
Consequently, relying solely on standard exposure management leaves you perpetually playing catch-up — like bringing a knife to a gunfight. You’re in a position where your exposure management pace and methods are simply not a match for the speed and scale of the multifaceted threats you face.
The Increasing Complexity of Cyber Threats
The modern threat landscape is characterized by highly sophisticated attack strategies. Attackers no longer rely solely on common malware and phishing techniques, not even in ransomware attacks. Instead, they employ advanced techniques, such as:
- Exploiting zero-day vulnerabilities, that is, previously unknown software flaws that defenses are often ill-equipped to handle.
- Carrying out multi-stage attacks (same as advanced persistent threats), which involve a series of well-coordinated steps to infiltrate networks, evade detection, and achieve objectives over an extended period.
- Compromising the software supply chain, in which attackers target third-party vendors to get access to their primary targets.
These types of attacks add layers of complexity, which can make attribution — immensely helpful in figuring out threat actors’ tactics, techniques, and procedures — much more challenging.
The rise of targeted and adaptive attacks makes things even worse.
Cybercriminals are moving away from broad, opportunistic campaigns towards tailored attacks designed to bypass the concrete security mechanisms of a specific organization. Thorough reconnaissance to understand their target’s defenses, identifying weaknesses, and even crafting custom exploits is the route these criminals are taking more and more often. This requires significant amounts of time and resources, which only shows the level of organization of today’s threat actors.
When we say that these attacks are adaptive, we mean that attackers modify their tactics and tools in real time based on the responses they encounter. This adaptability makes them frustratingly difficult to detect and neutralize with signature-based security solutions.
All this tells us one thing: We need a more dynamic and intelligent approach to protecting our digital assets.
How Autonomous Exposure Mitigation Works
Autonomous exposure mitigation works through continuous real-time threat detection and assessment as well as automated response and mitigation, and by helping you build self-healing IT systems.
Real-Time Threat Detection and Risk Assessment
Real-time threat detection and continuous risk assessment are the building blocks of autonomous exposure mitigation. They are effective because they are heavily powered by AI-driven analysis.
Machine learning models are in a position to continuously analyze petabytes of data across your entire digital environment. That can include everything from the software supply chain and legacy components to application behavior.
By constantly monitoring diverse data streams, AI engines can identify deviations from established baselines, recognize known malicious patterns, and flag vulnerabilities or misconfigurations as they emerge. The outcome is an up-to-date understanding of your risk landscape virtually at any point in time, unmediated by human intervention, informing a prompt response.
This thorough analysis is always complemented by behavioral analytics. Autonomous solutions go beyond looking for known threats. Instead, they establish normal patterns of activity for users, devices, and applications and assess the code and processes in your environment based on this baseline.
Through an uninterrupted assessment of behaviors, the system can detect anomalies or deviations that indicate suspicious or malicious activity, even if it doesn’t match a known threat signature. This capability allows for the identification of otherwise elusive indicators of compromise and helps you prevent cyberattacks altogether or, at least, address them in their early stages before they turn into severe security incidents that cause irreparable damage.
Automated Response and Remediation
Upon detecting suspicious activity or confirmed threats, an autonomous exposure mitigation capability can launch instant containment measures. Those can vary from isolating an infected endpoint to quarantining a compromised user account. Actions like these limit attackers’ ability to move laterally, gain higher privilege rights, and advance deeply in your environment.
But the response is not limited to containment. An exposure mitigation solution can block intrusion attempts as they happen. Constant monitoring, powered by a verified baseline, allows the solution to spot malicious code and processes—those that do not fit within the trusted baseline—without delays, blocking threats in milliseconds. (We can even argue that this doesn’t count as a response as much as it counts as prevention, but that’s a different discussion.)
The automated response mechanisms become even more powerful if they’re based on a zero-trust principle. For instance, with a “default-deny, allow-on-trust” principle at the center, nothing is inherently trusted in your environment. Code, processes, libraries, or configurations must be explicitly verified before being allowed to execute or access resources.
With this approach, the scope of threats that the security solution can autonomously address is extended to novel attacks, zero-day exploits, and malicious manipulations of trusted applications (e.g., living-off-the-land attacks).
Finally, coming to its core, autonomous exposure mitigation is invaluable for dynamic remediation. By remediation, we mean mitigation as a method of addressing vulnerabilities and other exposure. And not just any type, but mitigation applied immediately after deployment—as the very first step in managing exposure and reducing your attack surface.
The speed of this process guarantees exposures are addressed swiftly, preventing attackers from exploiting them. It also avoids the usual risks of untimely remediation by not depending on patching (typically, a long and complex process).
Thus, by promptly applying mitigation, you close the potential entry points for attackers, including those in legacy software and your software supply chain. This strengthens your overall security posture, regardless of the length of your patching cycles.
In other words, autonomous exposure mitigation has your back until you apply a critical patch, and can similarly provide robust security when there is a complete lack of a patch (in, say, legacy software).
Self-Healing Systems
Autonomous exposure mitigation supports the building of self-healing systems. Relying on a trusted baseline, it continuously performs corrective actions to restore your system to a known good state.
This restoration can come in different forms: rolling back unauthorized changes, reverting to earlier secure configurations, or even rebuilding compromised components from trusted images.
What’s more, thanks to its use of artificial intelligence and machine learning, an autonomous exposure mitigation tool can learn from past incidents to strengthen your defense against future attacks.
By autonomously identifying and rectifying security incidents or vulnerabilities, self-healing capabilities minimize the need for manual intervention in recovery processes. This results in enhanced resilience and reduced downtime following an attack or the discovery of a weakness.
The Benefits of Autonomous Exposure Mitigation
We could enumerate many different advantages when it comes to autonomous exposure mitigation, but why overextend the discussion when a few will suffice to illustrate its strength and potential?
Enhanced Speed and Efficiency
Much of what we’ve said so far has revolved around speed. Everything that autonomous exposure mitigation does—automated workflows, real-time detection, immediate response, AI or ML data analysis, and mitigation-first protection independent of patching—has one overarching goal: speed.
Okay, not just speed; efficiency as well. At least in cybersecurity, speed is a critical component of efficiency. If you’re not convinced, compare the automated analysis of disparate data from diverse sources to its manual counterpart. Or put autonomous mitigation and patching side-by-side. Indeed, the differences are striking.
Apart from speed, minimized human error is another aspect of efficient security. Inadvertent misconfigurations and errors, missed indicators of compromise and other oversights, and inconsistent application of security policies are all companions of manual tasks. Autonomous exposure mitigation allows you to avoid them as much as possible.
Continuous Protection
The continuous protection of autonomous exposure mitigation offers a tremendous advantage over traditional, human-centric security operations. Autonomous systems don’t work in shifts and don’t have bad days at work; they do their job around the clock without being limited by work schedules and fatigue.
They tirelessly analyze network traffic, system behavior, and security logs, making sure that threats don’t go undetected. They are addressed the moment they emerge, regardless of the time of day or staffing levels.
In addition to continuous protection, autonomous exposure mitigation employs self-learning systems adaptable to emerging threats. AI and machine learning allow the analysis of past attack patterns to identify anomalies and indicators of compromise associated with new and evolving attack vectors.
This adaptive capability is one of the key ingredients in the proactive neutralization of sophisticated threats. It brings a future-proof defense, compared to rule-based security solutions that require regular manual updates to address new threats.
Reduced Operational Costs
When you streamline and automate critical security operations, such as mitigation and response, you get a palpable decrease in operational costs.
For starters, you reduce the cost of reliance on large security teams dedicated to manual monitoring, analysis, and response. The existing security personnel can focus on more strategic initiatives and innovation rather than repetitive and time-consuming tasks. In other words, the efficiency gains from automation translate directly into lower staffing costs and reduced resource allocation for routine security operations.
On the other hand, since autonomous exposure mitigation prevents cyberattacks and minimizes their impact, you can enjoy an increase in productivity and avoid financial losses inextricably linked to security incidents. Successfully blocked attacks mean avoidance of costly downtime, and prevented data breaches allow you to dodge hefty legal penalties, regulatory fines, and irreparable reputational damage.
The proactive nature of autonomous exposure mitigation acts as a form of insurance. It defends your organization’s financial stability and secures business continuity by minimizing the costly consequences of cyber intrusions.
The Role of AI and Machine Learning in Autonomous Exposure Mitigation
AI and ML are not exclusive to autonomous exposure mitigation. However, they play such an important role in its implementation that they make up an integral part of its core.
AI’s Role in Predictive Threat Detection
AI plays a key role in predictive threat detection within the spectrum of autonomous exposure mitigation capabilities thanks to pattern recognition and anomaly detection.
As we’ve shown many times before, AI engines, particularly machine learning models, can analyze mountains of data, from application logs and code integrity to network traffic and user behavior. That allows them to establish a baseline of normal activity and detect obvious and not-so-obvious deviations.
This functionality makes them capable of detecting otherwise elusive threat indicators that can fly below the radars of overstretched security teams, enabling proactive protection that cuts threats at the roots.
Besides identifying and stopping ongoing attacks, AI also brings predictive capabilities to your table by analyzing historical incident data, threat intelligence feeds, and emerging trends. Machine learning algorithms can make inferences for the future based on past data, pinpointing the tactics, techniques, and procedures employed by threat actors.
By recognizing patterns and correlating them with the current state of your systems, an AI engine can anticipate future attack methods and even predict likely targets. The predictions can further serve as a basis for prioritizing your defenses in specific areas, patching weaknesses that are characteristic of your vertical, and adjusting security policies to face emerging threat trends well-prepared.
Machine Learning for Constant Improvement
As ML engines learn, they assess past mitigation mechanisms, which enables them to spot areas for improvement. That, in turn, refines their operations over time, helping them develop self-optimizing security measures. They can calibrate detection thresholds, improve response workflows, and revise security policies to evolve hand-in-hand with the changing threat landscape.
This constant improvement is additionally fueled by their ability to learn from real-world data. Machine learning models keep ingesting new threat intelligence, attack patterns observed in the wild, and the outcomes of security incidents in disparate environments. Learning from real-time data allows them to remain effective against highly-adaptive cyber adversaries in a fast-changing industry.
Conclusion
Autonomous exposure mitigation is essentially exposure management done right. It’s a step toward fulfilling the point of managing vulnerabilities and exposure—faster remediation in the form of immediate mitigation.
The key difference is that autonomous exposure mitigation goes beyond the limitations of standard, more reactive approaches, enabling you to use the power of AI and automation to monitor your systems constantly, respond to cyberattacks in real time, and nurture an IT environment with self-healing capabilities. Embracing its principles is necessary to navigate the complex maze of modern security threats.
Want to assess the merits of autonomous exposure mitigation and see how to reduce your attack surface without the delays of patching?
Book a demo and see OTTOGUARD.AI, the first workload patchless mitigation platform, in action.
FAQ Section
What is autonomous exposure mitigation?
Autonomous exposure mitigation is a cybersecurity approach that uses AI and automation to detect and mitigate security weaknesses, and respond to active threats in real time. It’s a proactive protection that reduces your organization’s attack surface by mitigating vulnerabilities and other exposures off the bat without human intervention.
How does autonomous exposure mitigation differ from other approaches, such as standard exposure management?
Autonomous exposure mitigation gives precedence to mitigation, automation, and immediate action over standard patching, manual workflows, and deferred remediation.
How can my organization start implementing autonomous exposure mitigation?
The easiest and simplest way for your organization to implement autonomous exposure mitigation is to find and deploy a platform that adopts a mitigation-first approach and offers real-time asset visibility, AI-powered data analysis, automated threat detection, and real-time threat response.
